AI for Complex Situations: Beyond Uniform Problem Solving

author: Michael Witbrock, IBM Thomas J. Watson Research Center
published: Aug. 23, 2017,   recorded: February 2017,   views: 1334

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The majority of recent technical advances in AI stem from problems that are structurally fairly uniform, but have complex and hard-to-describe patterns of variation within that structure. This structural uniformity characterizes, for example, speech signals up to transcription, text up to approximate translation, information extraction, video game play, lane-following, object labeling, and even the game of Go. However, many of the problems that we typically write programs for do not appear to be structurally uniform in this way: understanding a contract, or a regulation, and deciding how it affects a particular business process, is structurally complex: each detail of how the elements of the problem instance relate to one another is potentially critical. General reading comprehension, or automated programming seem similarly complex. If we are to produce AI systems that provide professional level assistance, we must address this complexity along with the variation. In this talk, I will discuss some of the complex, professional level problems we are attempting to address at IBM, and sketch some research paths, from both the past and possible future of AI.

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